In general, in the system in which a plurality of elevators go into commission, group management control is carried out. There are carried out therein various types of controls such as the assignment control for selecting an optimally assigned elevator in response to a call which has occurred in a hall. A forwarding operation is carried out in a peak time for a a specific floor differently from the occurrence of the call, and service zone may be divided.
In recent years, for example, as disclosed in Japanese Patent No. 2664766 or Japanese Patent Application Laid-open No. Hei 7-61723, there has been proposed a method of predicting the control result of the group management, i.e., group management performance such as waiting time and the like to set the control parameters.
In accordance with the above-mentioned two prior art publication, there is stated a system in which a neural network for receiving as its input traffic demand parameters and evaluation arithmetic operation parameters when carrying out the call assignment to output group management performance is employed, and the output result of the neural network is evaluated to set the optimal evaluation arithmetic operation parameter.
However, in the above-mentioned two articles relating to the prior art, a parameter which is set on the basis of the group management performance prediction result is limited to the single evaluation arithmetic operation parameter when carrying out the assignment. Thus, carrying out the arithmetic operation employing such a single evaluation arithmetic operation parameter when carrying out a call assignment leads in the limitation to the enhancement of the transport performance. That is, the various rule sets such as the forwarding operation and the zone division needs to be utilized depending on the traffic situation and hence excellent group management performance can not be obtained.
In addition, while the neural net has the advantage that its accuracy of arithmetic operation can be enhanced by learning, at the same time, it has also the disadvantage that it takes a lot of time for the accuracy of the arithmetic operation to reach a practical level.
In the system which is disclosed in the above-mentioned two articles relating to the prior art, it is impossible to obtain the expected group management performance unless the learning of the neural net is previously carried out in the factory. In addition, in a case where the traffic demand is abruptly changed due to a change of tenants in an associated building, it is possible to cope speedily with the change.
In the light of the foregoing, the present invention has been made in order to solve the above-mentioned problems associated with the prior art, and it is therefore an object of the present invention to provide an elevator group managing system which can select the optimal rule set in accordance with the performance prediction result to provide excellent service at all times.